CEE118X_HW5
Bill Li
10/23/2021
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|======================================================================| 100%#mapview(ces4_map, zcol = “Asthma”) # plot all the Asthma in map
You can use: class(ces4_clean\(`Census Tract`) to see whether the column is numeric data #You use: class(ca_tracts\)GEOID) to find out that GEOID is actually a character rather than numeric
you can use the function: as.numeric() to go from characters to number
use: str_pad(2, “left”, 0) to go from numeric to characters
The map above shows the high Asthma prevalence in California. It is the age-adjusted rate of emergency department visits for asthma. The data came from 2019 American Community Survey. As the color transition from dark purple to yellow, there is a higher rate of emergency visits for asthma. The coast of California has low asthma prevalence. High population density areas, including San Francisco, Los Angeles and San Diego, still face a high asthma prevalence. Central California also experience a relatively high asthma prevalence comparing to the coast.
The map above shows the concentrations of PM2.5 in California. It is the Annual mean PM 2.5 concentrations in the unit of microgram per cubic meters. The data came from 2019 American Community Survey. As the color transition from dark purple to yellow, there is an increasing in PM2.5 concentration. California coast has a relatively low PM2.5 concentration comparing to central California. High population density areas, including Los Angeles and San Diego, experience high PM2.5 concentrations. However, San Francisco doesn’t have a high PM2.5 concentration.
Based on the apparent best fit line, the is a slight positive correlation between PM2.5 and Asthma. However, one can also interpret as there is no correlation between PM2.5 and Asthma.
##
## Call:
## lm(formula = Asthma ~ PM2.5, data = ces4_map)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.346 -13.335 -5.208 9.570 156.830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -51.086 15.353 -3.327 0.000964 ***
## PM2.5 10.218 1.863 5.484 7.71e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.62 on 370 degrees of freedom
## Multiple R-squared: 0.07517, Adjusted R-squared: 0.07267
## F-statistic: 30.08 on 1 and 370 DF, p-value: 7.714e-08According to the chart
An increase of 1 microgram per cubic meters in PM2.5 is associated with an increase of 1.7228 in the rate of emergency department visit for asthma. The result can vary by 0.1564 in the rate of emergency department visit for asthma. Since our p value (Pr(>t)) is less than 5 percent, we can say that there is a relationship between PM2.5 and Asthma.
1.491% of the variation in Asthma is explained by the variation in PM2.5.
## [1] -15.68974
The residual distribution is heavily right skewed which means that our assumption and our model is invalid. The residual distribution needs to be normally distributed for a valid linear model.
##
## Call:
## lm(formula = log(Asthma) ~ PM2.5, data = ces4_map)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.70323 -0.35769 0.01364 0.41749 1.74734
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9074 0.4631 1.959 0.0508 .
## PM2.5 0.2931 0.0562 5.215 3.07e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5919 on 370 degrees of freedom
## Multiple R-squared: 0.06847, Adjusted R-squared: 0.06595
## F-statistic: 27.2 on 1 and 370 DF, p-value: 3.065e-07According to the chart
An increase of 1 microgram per cubic meters in PM2.5 is associated with an increase of exp(0.04387) or 1.045 in the rate of emergency department visit for asthma. The result can vary by exp(0.00295) or 1.00295 in the rate of emergency department visit for asthma. Since our p value (Pr(>t)) is less than 5 percent, we can say that there is a correlation between PM2.5 and Asthma.
2.682% of the variation in Asthma is explained by the variation in PM2.5.
## [1] -33.60545
## [1] -35.26078***Before switching to just Santa Clara County. The location with the most negative residual number is in Kern County, Bakersfield area. Negative value means that the actual value was less than the predicted value. In Bakersfield, our predicted association value between PM2.5 and hospital visits due to asthma is too high. We have an over-estimation of the relationship between PM2.5 and Asthma in the regions with negative residuals.